365天深度学习训练营-第P5周：运动鞋识别
- 本文为365天深度学习训练营 中的学习记录博客
- 参考文章：Pytorch实战 | 第P5周：运动鞋识别
- 原作者：K同学啊|接辅导、项目定制
要求：
了解如何设置动态学习率（重点）
调整代码使测试集accuracy到达84%。
拔高（可选）：
保存训练过程中的最佳模型权重
调整代码使测试集accuracy到达86%。
我的环境：
语言环境：Python3.8
编译器：Jupyter Lab
深度学习环境：Pytorch
数据集：K同学啊的百度网盘、和鲸
一、 前期准备
- 设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
如果设备上支持GPU就使用GPU,否则使用CPU
2. 导入数据
import os,PIL,random,pathlib
data_dir = './data/5-data/'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[2] for path in data_paths]
classeNames
# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
# transforms.RandomHorizontalFlip(), # 随机水平翻转
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
transforms.Normalize( # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
test_transform = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
transforms.Normalize( # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
train_dataset = datasets.ImageFolder("./data/5-data/train/",transform=train_transforms)
test_dataset = datasets.ImageFolder("./data/5-data/test/",transform=train_transforms)
train_dataset.class_to_idx
二、构建简单的CNN网络
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
for X, y in test_dl:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
三、 训练模型
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1=nn.Sequential(
nn.Conv2d(3, 12, kernel_size=5, padding=0), # 12*220*220
nn.BatchNorm2d(12),
nn.ReLU())
self.conv2=nn.Sequential(
nn.Conv2d(12, 12, kernel_size=5, padding=0), # 12*216*216
nn.BatchNorm2d(12),
nn.ReLU())
self.pool3=nn.Sequential(
nn.MaxPool2d(2)) # 12*108*108
self.conv4=nn.Sequential(
nn.Conv2d(12, 24, kernel_size=5, padding=0), # 24*104*104
nn.BatchNorm2d(24),
nn.ReLU())
self.conv5=nn.Sequential(
nn.Conv2d(24, 24, kernel_size=5, padding=0), # 24*100*100
nn.BatchNorm2d(24),
nn.ReLU())
self.pool6=nn.Sequential(
nn.MaxPool2d(2)) # 24*50*50
self.dropout = nn.Sequential(
nn.Dropout(0.2))
self.fc=nn.Sequential(
nn.Linear(24*50*50, len(classeNames)))
def forward(self, x):
batch_size = x.size(0)
x = self.conv1(x) # 卷积-BN-激活
x = self.conv2(x) # 卷积-BN-激活
x = self.pool3(x) # 池化
x = self.conv4(x) # 卷积-BN-激活
x = self.conv5(x) # 卷积-BN-激活
x = self.pool6(x) # 池化
x = self.dropout(x)
x = x.view(batch_size, -1) # flatten 变成全连接网络需要的输入 (batch, 24*50*50) ==> (batch, -1), -1 此处自动算出的是24*50*50
x = self.fc(x)
return x
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
model = Model().to(device)
model
Model(
(conv1): Sequential(
(0): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(pool3): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv4): Sequential(
(0): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv5): Sequential(
(0): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(pool6): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(dropout): Sequential(
(0): Dropout(p=0.2, inplace=False)
)
(fc): Sequential(
(0): Linear(in_features=60000, out_features=2, bias=True)
)
)
- 编写训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
- 编写测试函数
测试函数和训练函数大致相同，但是由于不进行梯度下降对网络权重进行更新，所以不需要传入优化器
def test (dataloader, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
- 设置动态学习率
✨调用官方动态学习率接口
与上面方法是等价的
def adjust_learning_rate(optimizer, epoch, start_lr):
# 每 2 个epoch衰减到原来的 0.98
lr = start_lr * (0.92 ** (epoch // 2))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
learn_rate = 1e-4 # 初始学习率
optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
- 正式训练
loss_fn = nn.CrossEntropyLoss() # 创建损失函数
epochs = 40
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):
# 更新学习率（使用自定义学习率时使用）
adjust_learning_rate(optimizer, epoch, learn_rate)
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
# scheduler.step() # 更新学习率（调用官方动态学习率接口时使用）
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# 获取当前的学习率
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,
epoch_test_acc*100, epoch_test_loss, lr))
print('Done')
Epoch: 1, Train_acc:53.8%, Train_loss:0.749, Test_acc:51.3%, Test_loss:0.719, Lr:1.00E-04
Epoch: 2, Train_acc:61.4%, Train_loss:0.659, Test_acc:63.2%, Test_loss:0.600, Lr:1.00E-04
Epoch: 3, Train_acc:64.5%, Train_loss:0.634, Test_acc:71.1%, Test_loss:0.554, Lr:9.20E-05
Epoch: 4, Train_acc:69.9%, Train_loss:0.578, Test_acc:76.3%, Test_loss:0.541, Lr:9.20E-05
Epoch: 5, Train_acc:75.3%, Train_loss:0.534, Test_acc:73.7%, Test_loss:0.517, Lr:8.46E-05
Epoch: 6, Train_acc:75.7%, Train_loss:0.505, Test_acc:77.6%, Test_loss:0.488, Lr:8.46E-05
Epoch: 7, Train_acc:78.3%, Train_loss:0.477, Test_acc:77.6%, Test_loss:0.527, Lr:7.79E-05
Epoch: 8, Train_acc:77.3%, Train_loss:0.474, Test_acc:71.1%, Test_loss:0.499, Lr:7.79E-05
Epoch: 9, Train_acc:81.5%, Train_loss:0.456, Test_acc:78.9%, Test_loss:0.465, Lr:7.16E-05
Epoch:10, Train_acc:82.5%, Train_loss:0.429, Test_acc:81.6%, Test_loss:0.478, Lr:7.16E-05
Epoch:11, Train_acc:83.7%, Train_loss:0.417, Test_acc:81.6%, Test_loss:0.450, Lr:6.59E-05
Epoch:12, Train_acc:85.1%, Train_loss:0.395, Test_acc:82.9%, Test_loss:0.481, Lr:6.59E-05
Epoch:13, Train_acc:84.3%, Train_loss:0.387, Test_acc:82.9%, Test_loss:0.449, Lr:6.06E-05
Epoch:14, Train_acc:87.5%, Train_loss:0.362, Test_acc:81.6%, Test_loss:0.455, Lr:6.06E-05
Epoch:15, Train_acc:87.5%, Train_loss:0.362, Test_acc:82.9%, Test_loss:0.436, Lr:5.58E-05
Epoch:16, Train_acc:88.6%, Train_loss:0.353, Test_acc:82.9%, Test_loss:0.431, Lr:5.58E-05
Epoch:17, Train_acc:89.2%, Train_loss:0.350, Test_acc:82.9%, Test_loss:0.419, Lr:5.13E-05
Epoch:18, Train_acc:87.8%, Train_loss:0.346, Test_acc:81.6%, Test_loss:0.404, Lr:5.13E-05
Epoch:19, Train_acc:89.8%, Train_loss:0.322, Test_acc:80.3%, Test_loss:0.443, Lr:4.72E-05
Epoch:20, Train_acc:87.3%, Train_loss:0.340, Test_acc:84.2%, Test_loss:0.452, Lr:4.72E-05
Epoch:21, Train_acc:89.2%, Train_loss:0.336, Test_acc:82.9%, Test_loss:0.434, Lr:4.34E-05
Epoch:22, Train_acc:91.8%, Train_loss:0.314, Test_acc:81.6%, Test_loss:0.436, Lr:4.34E-05
Epoch:23, Train_acc:92.4%, Train_loss:0.306, Test_acc:81.6%, Test_loss:0.402, Lr:4.00E-05
Epoch:24, Train_acc:90.6%, Train_loss:0.309, Test_acc:81.6%, Test_loss:0.445, Lr:4.00E-05
Epoch:25, Train_acc:92.6%, Train_loss:0.295, Test_acc:84.2%, Test_loss:0.377, Lr:3.68E-05
Epoch:26, Train_acc:92.4%, Train_loss:0.298, Test_acc:82.9%, Test_loss:0.412, Lr:3.68E-05
Epoch:27, Train_acc:93.2%, Train_loss:0.298, Test_acc:81.6%, Test_loss:0.389, Lr:3.38E-05
Epoch:28, Train_acc:93.4%, Train_loss:0.284, Test_acc:81.6%, Test_loss:0.426, Lr:3.38E-05
Epoch:29, Train_acc:93.2%, Train_loss:0.284, Test_acc:84.2%, Test_loss:0.405, Lr:3.11E-05
Epoch:30, Train_acc:94.0%, Train_loss:0.278, Test_acc:82.9%, Test_loss:0.452, Lr:3.11E-05
Epoch:31, Train_acc:92.8%, Train_loss:0.286, Test_acc:82.9%, Test_loss:0.419, Lr:2.86E-05
Epoch:32, Train_acc:94.4%, Train_loss:0.275, Test_acc:82.9%, Test_loss:0.434, Lr:2.86E-05
Epoch:33, Train_acc:95.2%, Train_loss:0.269, Test_acc:82.9%, Test_loss:0.385, Lr:2.63E-05
Epoch:34, Train_acc:94.2%, Train_loss:0.269, Test_acc:82.9%, Test_loss:0.412, Lr:2.63E-05
Epoch:35, Train_acc:93.2%, Train_loss:0.273, Test_acc:82.9%, Test_loss:0.426, Lr:2.42E-05
Epoch:36, Train_acc:94.2%, Train_loss:0.275, Test_acc:82.9%, Test_loss:0.386, Lr:2.42E-05
Epoch:37, Train_acc:94.4%, Train_loss:0.262, Test_acc:82.9%, Test_loss:0.393, Lr:2.23E-05
Epoch:38, Train_acc:95.2%, Train_loss:0.264, Test_acc:82.9%, Test_loss:0.375, Lr:2.23E-05
Epoch:39, Train_acc:94.2%, Train_loss:0.265, Test_acc:82.9%, Test_loss:0.422, Lr:2.05E-05
Epoch:40, Train_acc:94.6%, Train_loss:0.257, Test_acc:82.9%, Test_loss:0.379, Lr:2.05E-05
Done
四、 结果可视化
7. Loss与Accuracy图
import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 #分辨率
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
output_28_0.png
- 指定图片进行预测
⭐torch.squeeze()详解
对数据的维度进行压缩，去掉维数为1的的维度
函数原型：
torch.squeeze(input, dim=None, *, out=None)
关键参数说明：
input (Tensor)：输入Tensor
dim (int, optional)：如果给定，输入将只在这个维度上被压缩
实战案例：
⭐torch.unsqueeze()
对数据维度进行扩充。给指定位置加上维数为一的维度
函数原型：
torch.unsqueeze(input, dim)
关键参数说明：
input (Tensor)：输入Tensor
dim (int)：插入单例维度的索引
实战案例：
from PIL import Image
classes = list(train_dataset.class_to_idx)
def predict_one_image(image_path, model, transform, classes):
test_img = Image.open(image_path).convert('RGB')
# plt.imshow(test_img) # 展示预测的图片
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_,pred = torch.max(output,1)
pred_class = classes[pred]
print(f'预测结果是：{pred_class}')
# 预测训练集中的某张照片
predict_one_image(image_path='./5-data/test/adidas/1.jpg',
model=model,
transform=train_transforms,
classes=classes)
五、保存并加载模型
# 模型保存
PATH = './model.pth' # 保存的参数文件名
torch.save(model.state_dict(), PATH)
# 将参数加载到model当中
model.load_state_dict(torch.load(PATH, map_location=device))
六、动态学习率
9. torch.optim.lr_scheduler.StepLR
等间隔动态调整方法，每经过step_size个epoch，做一次学习率decay，以gamma值为缩小倍数。
函数原型：
torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)
关键参数详解：
optimizer(Optimizer)：是之前定义好的需要优化的优化器的实例名
step_size(int)：是学习率衰减的周期，每经过每个epoch，做一次学习率decay
gamma(float)：学习率衰减的乘法因子。Default：0.1
用法示例：
optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
- lr_scheduler.LambdaLR
根据自己定义的函数更新学习率。
函数原型：
torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose=False)
关键参数详解：
optimizer(Optimizer)：是之前定义好的需要优化的优化器的实例名
lr_lambda(function)：更新学习率的函数
用法示例：
lambda1 = lambda epoch: (0.92 ** (epoch // 2) # 第二组参数的调整方法
optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1) #选定调整方法
- lr_scheduler.MultiStepLR
在特定的 epoch 中调整学习率
函数原型：
torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1, verbose=False)
关键参数详解：
optimizer(Optimizer)：是之前定义好的需要优化的优化器的实例名
milestones(list)：是一个关于epoch数值的list，表示在达到哪个epoch范围内开始变化，必须是升序排列
gamma(float)：学习率衰减的乘法因子。Default：0.1
用法示例：
Python
复制代码
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optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[2,6,15], #调整学习率的epoch数
gamma=0.1)
更多的官方动态学习率设置方式可参考：https://pytorch.org/docs/stable/optim.html
调用官方接口示例：
model = [Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = SGD(model, 0.1)
scheduler = ExponentialLR(optimizer, gamma=0.9)
for epoch in range(20):
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
scheduler.step()